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Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
Abstract Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
Abstract Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
Basu, Mousumi (author)
Journal of The Institution of Engineers (India): Series B ; 97 ; 525-535
2015-07-22
11 pages
Article (Journal)
Electronic Resource
English
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